class face_detect(object):
def __init__(self):
self.load_model()
def load_model(self):
thresh = [0.6, 0.7, 0.7]
min_face_size = 20
stride = 2
slide_window = False
detectors = [None, None, None]
prefix = [
'./weight/PNet_landmark/PNet', './weight/RNet_landmark/RNet',
'./weight/ONet_landmark/ONet'
]
epoch = [18, 14, 16]
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
PNet, RNet, ONet = FcnDetector(P_Net, model_path[0]), Detector(R_Net, 24, 1, model_path[1]), \
Detector(O_Net, 48, 1, model_path[2])
detectors[0], detectors[1], detectors[2] = PNet, RNet, ONet
self.mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
def detect(self, img, show_pic=False):
"""
:param img: BGR格式的图片
:param show_pic: 是否展示图片
:return:boxes_c type is ndarray, shape is (M, 5)
"""
boxes_c, landmarks = self.mtcnn_detector.detect(img)
if show_pic:
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [
int(bbox[0]),
int(bbox[1]),
int(bbox[2]),
int(bbox[3])
]
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]),
(corpbbox[2], corpbbox[3]), (255, 0, 0), 2)
cv2.putText(frame, '{:.3f}'.format(score),
(corpbbox[0], corpbbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 3)
for i in range(landmarks.shape[0]):
for j in range(int(len(landmarks[i]) / 2)):
cv2.circle(frame, (int(landmarks[i][2 * j]),
int(int(landmarks[i][2 * j + 1]))), 2,
(0, 0, 255))
cv2.putText(frame, 'handsome men', (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (127, 255, 212), 2)
cv2.imshow("", frame)
if cv2.waitKey(0) & 0xFF == ord('q'):
cv2.destroyAllWindows()
return boxes_c
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh)
#设定输出路径
out_path = config.out_path
#两种输入方式，1为图片，2为摄像头
if config.input_mode == '1':
#设定测试图片的路径
path = config.test_dir
#print(path)
for item in os.listdir(path):
img_path = os.path.join(path, item)
img = cv2.imread(img_path)
#获知图片中的人脸框和关键点
boxes_c, landmarks = mtcnn_detector.detect(img)
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])]
#画人脸框
cv2.rectangle(img, (corpbbox[0], corpbbox[1]),
(corpbbox[2], corpbbox[3]), (255, 0, 0), 1)
#判别为人脸的置信度
cv2.putText(img, '{:.2f}'.format(score),
(corpbbox[0], corpbbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
#画关键点
for i in range(landmarks.shape[0]):
for j in range(len(landmarks[i]) // 2):
cv2.circle(img, (int(
start_path = '/home/sherk/Workspace/mtcnn-pytorch/'
filenames = os.listdir(start_path + 'img')
missing_detection = 0
false_detection = 0
all_detection = 0
all_labels = 0
for filename in tqdm(filenames):
iou_threshold = 0.4
image = start_path + 'img/{}'.format(filename)
img = cv2.imread(image)
boxes_det, _ = mtcnn_detector.detect(img)
boxes_det = boxes_det.astype(np.int)
xml_file = start_path + 'anno/{}.xml'.\
format( '.'.join( filename.split('.')[:-1] ) )
boxes_lab = boxes_extract(xml_file)
if boxes_lab is None:
if boxes_det is not None:
false_detection += len(boxes_det)
all_detection += len(boxes_det)
continue
if boxes_det.shape[0] == 0:
if boxes_lab is not None:
return True if ((xs > bbox[0]).all() & (xs < bbox[2]).all()) &\
((ys > bbox[1]).all() & (ys < bbox[3]).all()) else False
#%% generate the landmark file
celeba_landmark_list = open('data/celeba_trainImageList.txt', 'w')
err_idx = 0
for i in tqdm(range(sub_labels.shape[0])):
src_img = join(img_dir, sub_labels.image_id.iloc[i])
src_write = join('Img/img_celeba', sub_labels.image_id.iloc[i])
# des_img = join(out_path, sub_labels.image_id.iloc[i])
img = cv2.imread(src_img)
bboxes, landmarks = mtcnn_detector.detect(img)
label_landmark = sub_labels.iloc[i, -10:].values
if (bboxes.shape[0] > 0):
if (in_box(label_landmark, bboxes[0, :])):
bboxes_list = [
str(x) for x in list(bboxes[0, [0, 2, 1, 3]].astype(np.int))
]
landmarks_list = [str(x) for x in list(label_landmark)]
ln = ' '.join([src_write] + bboxes_list + landmarks_list) + '\n'
celeba_landmark_list.write(ln)
else:
err_idx += 1
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
video_capture = cv2.VideoCapture(0)
video_capture.set(3, 500)
video_capture.set(4, 600)
corpbbox = None
while True:
# fps = video_capture.get(cv2.CAP_PROP_FPS)
t1 = cv2.getTickCount()
ret, frame = video_capture.read()
if ret:
image = np.array(frame)
boxes_c, landmarks = mtcnn_detector.detect(image)
print(landmarks.shape)
t2 = cv2.getTickCount()
t = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0 / t
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])]
# if score > thresh:
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]),
(corpbbox[2], corpbbox[3]), (255, 0, 0), 2)
cv2.putText(frame, '{:.3f}'.format(score),
(corpbbox[0], corpbbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 3)